Hoifung Poon

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

2021 · ACM Transactions on Computing for Healthcare

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A …

Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing

2021 · arXiv (Cornell University)

Motivation: A perennial challenge for biomedical researchers and clinical practitioners is to stay abreast with the rapid growth of publications and medical notes. Natural language processing (NLP) has emerged as a promising direction for taming …

CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation

2025 · arXiv (Cornell University)

The National Comprehensive Cancer Network (NCCN) provides evidence-based guidelines for cancer treatment. Translating complex patient presentations into guideline-compliant treatment recommendations is time-intensive, requires specialized expertise, and is prone to error. Advances in large language model …

Representing Text for Joint Embedding of Text and Knowledge Bases

2015

Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base …

Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text

2016

Modeling relation paths has offered significant gains in embedding models for knowledge base (KB) completion. However, enumerating paths between two entities is very expensive, and existing approaches typically resort to approximation with a sampled subset. …

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

2017 · arXiv (Cornell University)

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. …

Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision

2018

Deep learning has emerged as a versatile tool for a wide range of NLP tasks, due to its superior capacity in representation learning. But its applicability is limited by the reliance on annotated examples, which …

Document-Level N-ary Relation Extraction with Multiscale Representation Learning

2019

Robin Jia, Cliff Wong, Hoifung Poon. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

Cross-Sentence <i>N</i>-ary Relation Extraction with Graph LSTMs

2017 · Transactions of the Association for Computational Linguistics

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. …

Adversarial Training for Large Neural Language Models

2020 · arXiv (Cornell University)

Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models …

BioGPT: generative pre-trained transformer for biomedical text generation and mining

2022 · Briefings in Bioinformatics

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language …